Data-Driven Decentralized Online System Identification-Based Integral Model-Predictive Voltage and Frequency Control in Microgrids
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Automation & Control Systems↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Invited Review Article: Advanced light microscopy for biological space research
As commercial space flights have become feasible and long-term extraterrestrial missions are planned, it is imperative that the impact of space travel and the space environment on human physiology be thoroughly characterized. Scrutinizing the effects of potentially detrimental factors such as ionizing radiation and microgravity at the cellular and tissue level demands adequate visualization technology. Advanced light microscopy (ALM) is the leading tool for non-destructive structural and functional investigation of static as well as dynamic biological systems. In recent years, technological developments and advances in photochemistry and genetic engineering have boosted all aspects of resolution, readout and throughput, rendering ALM ideally suited for biological space research. While various microscopy-based studies have addressed cellular response to space-related environmental stressors, biological endpoints have typically been determined only after the mission, leaving an experimental gap that is prone to bias results. An on-board, real-time microscopical monitoring device can bridge this gap. Breadboards and even fully operational microscope setups have been conceived, but they need to be rendered more compact and versatile. Most importantly, they must allow addressing the impact of gravity, or the lack thereof, on physiologically relevant biological systems in space and in ground-based simulations. In order to delineate the essential functionalities for such a system, we have reviewed the pending questions in space science, the relevant biological model systems, and the state-of-the art in ALM. Based on a rigorous trade-off, in which we recognize the relevance of multi-cellular systems and the cellular microenvironment, we propose a compact, but flexible concept for space-related cell biological research that is based on light sheet microscopy.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Advanced Kalman Filter for Real-Time Responsiveness in Complex Systems
Complex engineering systems pose fundamental challenges in real-time operations and control because they are highly dynamic systems consisting of a large number of elements with severe nonlinearities and discontinuities. Today’s tools for real-time complex system operations are mostly based on steady state models, unable to capture the dynamic nature and too slow to prevent system failures. We developed advanced Kalman filtering techniques and the formulation of dynamic state estimation using Kalman filtering techniques to capture complex system dynamics in aiding real-time operations and control. In this work, we looked at complex system issues including severe nonlinearity of system equations, discontinuities caused by system controls and network switches, sparse measurements in space and time, and real-time requirements of power grid operations. We sought to bridge the disciplinary boundaries between Computer Science and Power Systems Engineering, by introducing methods that leverage both existing and new techniques. While our methods were developed in the context of electrical power systems, they should generalize to other large-scale scientific and engineering applications.
97 MATHEMATICS AND COMPUTING↗
Digital Analytics, Causal Knowledge Acquisition and Reasoning for Technical Language Processing
Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.
Mandelli, Diego [Idaho National Laboratory (INL), ↗
Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application
As renewable power generation deployment increases, fossil fuel plants are increasingly required to operate more flexibly. Many coal-fired power plants were originally designed to operate at base load and do not operate optimally at partial load. Predictive first-principles plant-wide models can be employed to identify opportunities for flexibility improvements and diagnose low-load operating issues. This paper describes the application of the Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) to model and optimize flexible power plant operations. The key benefits of using IDAES are that it provides an open-source, fully equation-oriented modeling framework for efficient modular model construction, reuse, and customization, together with a mathematical optimization framework leveraging powerful, state-of-the-art solvers. The process systems engineering workflow from predictive process simulation to parameter estimation, model validation, and plant optimization is applicable to a variety of existing and next-generation energy systems as well as other chemical and environmental processes. Here, to demonstrate this capability, a physics-based, steady-state model was developed to improve full- and part-load performance of the Escalante Generating Station, a 245 MWe (net) subcritical pulverized coal-fired power plant owned and operated by Tri-State Generation and Transmission Association. Specifically, sixty-nine model parameters were simultaneously estimated from several months of operating data enabling prediction of flow rates, temperatures, pressures, and steam quality throughout the plant. The validated model was leveraged by Escalante to reduce the minimum operating load from 90 MW to 50 MW by diagnosing a low-load water-hammer issue, enabling coal usage and emissions reductions during periods of low power demand. Additionally, opportunities for heat rate reduction (i.e., efficiency improvement) through a steeper sliding-pressure approach to load-following and optimization of other boiler operating variables were also identified and quantified. For example, a potential efficiency improvement of 0.7 percentage points was observed at half-load operation.
01 COAL, LIGNITE, AND PEAT↗
Think before you throw! An analysis of behavioral interventions targeting PET bottle recycling in the United States
The United States generates 42 Mt of plastic waste each year and is one of the biggest contributors to ocean plastic waste. Consequently, plastic has become synonymous with the linear economy, and many scholars are studying and proposing circular economy solutions to mitigate plastic pollution. Recycling has received much attention from both social sciences and engineering as a circular economy strategy, but no study has yet quantified how behavioral interventions could asymmetrically affect different populations. Here, this study combines agent-based modeling, material flow analysis, system dynamics, and life cycle assessment to assess the effect of four behavioral interventions on the collection rates of polyethylene terephthalate bottle waste, displaced virgin plastic manufacturing, and avoided greenhouse gas (GHG) emissions. Results show that, while behavioral interventions would require about 300–900 GJ of additional energy at end-of-life due to improved collection rates, they would avoid about 500–700 thousand metric tons of GHG emissions. Results also illustrate the importance of habits in disposal behaviors and show that different forms of interventions can be better adapted to particular social contexts than others. While the circular economy and its application to plastic waste should certainly not be restricted to recycling, this study demonstrates that improved collection rates and recycling technologies can contribute to reducing the amount of plastic waste polluting our oceans.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Interfacial Engineering for Highly Efficient-Conjugated Polymer-Based Bulk Heterojunction Photovoltaic Devices
The aim of our proposal is to apply interface engineering approach to improve charge extraction, guide active layer morphology, improve materials compatibility, and ultimately allow the fabrication of high efficiency tandem cells. Specifically, we aim at developing: i. Interfacial engineering using small molecule self-assembled monolayers; ii. Nanostructure engineering in OPVs using polymer brushes; iii. Development of efficient light harvesting and high mobility materials for OPVs; iv. Physical characterization of the nanostructured systems using electrostatic force microscopy, and conducting atomic force microscopy; v. All-solution processed organic-based tandem cells using interfacial engineering to optimize the recombination layer currents; vi. Theoretical modeling of charge transport in the active semiconducting layer. The material development effort is guided by advanced computer modeling and surface/ interface engineering tools to allow us to obtain better understanding of the effect of electrode modifications on OPV performance for the investigation of more elaborate device structures. The materials and devices developed within this program represent a major conceptual advancement using an integrated approach combining rational molecular design, material, interface, process, and device engineering to achieve solar cells with high efficiency, stability, and the potential to be used for large-area roll-to-roll printing. This may create significant impact in lowering manufacturing cost of polymer solar cells for promoting clean renewable energy use and preventing the side effects from using fossil fuels to impact environment.
14 SOLAR ENERGY↗
Dysfunctionality Hazard Curve: Risk-Based Tool to Support the Resilient Design of Systems Subjected to Multihazards
While resilience metrics have been proposed and studied given a functionality recovery curve, they have not emphasized enough on accounting for the uncertainties in the multihazard occurrences and intensities. Moreover, these resilience metrics are not risk-based (i.e., they do not express the system's resilience loss as a frequency of exceedance), leading to inconsistencies in system performance description when compared to performance-based engineering frameworks. A risk-based tool termed dysfunctionality hazard curve is proposed to assess the resilience of systems subjected to single hazards or multihazards with inter-event dependencies. Dysfunctionality hazard curve expresses system resilience performance as frequency of exceedance of time to full functionality. In doing so, it characterizes system recovery as a sequence of repair activities and also considers the uncertainties in the multihazard occurrences and intensities. Dysfunctionality hazard curve is demonstrated for a residential building susceptible to earthquake and hurricane hazards. Results indicate that Dysfunctionality hazard curve for earthquakes is greater than that for hurricane winds under single hazards. Under multihazards, considering inter-event dependencies during system recovery leads to larger dysfunctionality hazard curve than ignoring them. Finally, the concept of dysfunctionality hazard curve is also extended to a system-of-systems consisting of residential and commercial buildings.
42 ENGINEERING↗
Development of Integrated Thermal Fluids Modeling Capability for MSRs
The DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program supports a full range of computational thermal fluids analysis capabilities and code developments for a broad class of light-water and advanced reactor concepts. The research and development approach under thermal fluids technical area synergistically combines three length and time scales in a hierarchal multi-scale approach. To demonstrate the feasibility and capabilities of a multi-scale thermal fluids capability using these codes, a key joint effort has been pursued to develop an integrated system-and engineering-scale thermal fluids analysis capability with the MOOSE-based codes, through integration of SAM and Pronghorn, both based on the MOOSE framework. This report summarizes the progress in developing an integrated system- and engineering-scale thermal fluids analysis capability based on SAM and Pronghorn for molten salt reactors (MSRs), which gained significant interest in recently years. Two coupling approaches were studied, i.e. separate domain or domain-segregated coupling approach and the domain-overlapping approach. A series of coupled multi-physics models have been developed for a common reference molten salt fast reactor (MSFR) concept, ranging from standalone SAM system model to integrated SAM-Pronghorn-Griffin models. Both the steady state and the transient simulations are performed to the state-of-the-art simulation capabilities of NEAMS software suite in MSFR system applications. This report also covers further development and testing of the gas transport model in SAM for MSR modeling support. The presence of noncondensable gas in MSR systems would have strong impacts on fission gas removal and transport of noble metals throughout the system. Fission products removed through the off-gas system can also impact reactivity and can act as an additional point of heat removal. To ensure that the gas transport model is adequately tested, the supported modeling capabilities and features of SAM were identified, and a suite of tests were developed to test the model for each identified feature. Validation and UQ testing were also performed for the model and demonstrated that the buoyancy term, which was originally developed using non-salt/helium gas experimental data, can capture the experimental gas bubble velocity and diameter to within experimental and code uncertainty. An existing MSRE model was also modified in this work to include the gas transport model and to demonstrate the gas model behavior for realistic conditions of interest.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Scientific Discovery with Physics-Informed System Identification (Abbreviated Report)
My fellowship research focused on making physics-based simulations faster and more useful through machine learning. Many problems in science and engineering are governed by partial differential equations, but high-fidelity simulations are often too expensive to run repeatedly. I worked on improving Latent Space Dynamics Identification (LaSDI), a reduced-order modeling framework that compresses large simulation data sets into a smaller representation and then learns how that representation evolves over time. The motivation was to develop reduced models that remain accurate for more challenging systems, especially when predictions must remain reliable over long time intervals or when the underlying dynamics are more complicated than standard methods can easily handle. I also contributed to related work on Quandary, a high-performance software effort for simulation and control of open quantum systems, before focusing primarily on Latent Space Dynamics Identification methods. The main outcomes of the fellowship were two new algorithms (both of which were published), Rollout-LaSDI and Higher-Order LaSDI, together with supporting work on multi-stage Latent Space Dynamics Identification. Rollout-LaSDI improved long-term prediction by training the model to stay accurate over extended time horizons, and Higher-Order LaSDI broadened the method so it could model systems with higher-order time dynamics. My contributions to multistage Latent Space Dynamics Identification also helped show that its later training stages could be simplified without losing effectiveness, and that this behavior held across different model architectures and training strategies. Taken together, these advances improved the accuracy, flexibility, and practical value of reduced-order modeling tools for computational science.
97 MATHEMATICS AND COMPUTING↗
Improving Energy Efficiency by Developing Components for Distributed Cooling and Heating Based on Thermal Comfort Modeling (General Motors LLC Final Project Report)
On November 3, 2009, General Motors (GM) accepted U.S. Department of Energy (DOE) Cooperative Agreement award number DE-EE0000014 from the National Energy Technology Laboratory (NETL). GM was selected to execute a three-year cost shared research and development project on Solid State Energy Conversion for Vehicular Heating, Ventilation & Air Conditioning (HVAC) and for Waste Heat Recovery. The primary objective of this project was to develop a distributed automotive HVAC system that provides thermal comfort equivalent to current state-of-the-art centralized systems while using significantly less energy. To satisfy this objective, the team identified the following goals: • Reduce by at least 30% of the “billions of gallons” the fuel used in cooling mode to maintain occupant comfort through the localized use of thermoelectric (TE) technology while maintaining occupant comfort and safety. • Develop TE HVAC components with a coefficient of performance (COP) greater than 1.3 for cooling and greater than 2.3 for heating, then integrate and test these components as part of a reliable distributed HVAC system in a conventional vehicle (an eAssist Buick Lacrosse) and an extended range electric vehicle (a Chevrolet Volt). • Update the UCB Thermal Comfort model to predict the response of vehicle occupants to localized heating and cooling based on human subject testing, and develop associated computer-aided engineering (CAE) tools to support the integration of distributed HVAC components into future vehicle designs. A secondary objective of the project was to improve the efficiency of thermoelectric generators for converting engine waste heat into electricity.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Manganese-Based Magnets: Manganese-Based Permanent Magnet with 40 MGOe at 200°C
REACT Project: PNNL is working to reduce the cost of wind turbines and EVs by developing a manganese-based nano-composite magnet that could serve as an inexpensive alternative to rare-earth-based magnets. The manganese composite, made from low-cost and abundant materials, could exceed the performance of today’s most powerful commercial magnets at temperature higher than 200°C. Members of PNNL’s research team will leverage comprehensive computer high-performance supercomputer modeling and materials testing to meet this objective. Manganese-based magnets could withstand higher temperatures than their rare earth predecessors and potentially reduce the need for any expensive, bulky engine cooling systems for the motor and generator. This would further contribute to cost savings for both EVs and wind turbines.
ARPA-E↗
Development of Non Destructive Assay Approach for Characterization of Spent Fuel Debris at Fukushima - 18543
Fukushima is considering options for decommissioning the damaged reactor. One of the technical challenges is the accurate characterization of fissile isotopes in the spent fuel debris. This is due to the fissile content, high gamma and neutron emission and uncertainties regarding the fuel burnup and unknown, heterogeneous mixture of materials that may be present. These requirements lead to significant challenges for potential measurement approaches with regard to the requirements for shielding and remote operation that result from plant and operational constraints. Mirion Technologies (MTKK) has been working to perform feasibility and concept design studies for a non-destructive assay (NDA) system that could be used to characterize the fissile content of retrieved samples of fuel debris. The main advantage of using an NDA technique is that the entire volume of the sample is interrogated so the method is not reliant upon assumptions of representativeness of samples retrieved and sent for laboratory analysis. The proposed technique comprises a combination of gamma spectroscopy and combined passive / active neutron interrogation with advanced data analysis algorithms in order to determine the maximum available data on the spent fuel properties and obtain a full radionuclide characterization of each sample. This includes quantification of individual fissile nuclides with validated uncertainties. In this paper we describe the NDA techniques and systems that are proposed, their benefits and limitations, and how they may be implemented into a full-scale system for fuel element debris characterization. We describe the studies that were performed based on experience of real systems and research supplemented with extensive computer modelling work to represent the expected fuel element debris properties. The work benefits from Mirion's extensive global physics and engineering expertise in designing, supplying and operating a wide range of NDA systems for diverse applications and in different challenging physical and operational environments. We describe how the advanced and innovative physics techniques that have resulted from Mirion's collective experience in these areas, can be deployed in an NDA system. The final approach that has been developed provides a means for determining the masses of the individual fissile isotopes with robust uncertainties and capable of implementation into an NDA system that meets the expected requirements for an operational plant. We focus on the techniques that have been developed and describe how they are of wider relevance and interest for projects that include challenges for characterisation of fuel residues. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Reinforcement learning for adaptive maintenance policy optimization under imperfect knowledge of the system degradation model and partial observability of system states
Maintenance policy optimization usually is faced with challenges that arise from an imperfect knowledge of system degradation models and from the partial observability of system degradation states. Here, this paper proposes a reinforcement learning method to address these two challenges for a class of maintenance problems with Markov degradation processes. The reinforcement learning approach consists of a learning component and a planning component. Using sequentially collected observations, at each step of decision-making the learning component improves the knowledge of system degradation in terms of the probability distributions of the transition rates based on sequential Bayesian inference. Using the updated transition rates, at each step of decision-making the maintenance policy optimization problem is then formulated as a partially observable Markov decision problem, and the planning component computes the optimal maintenance policy that maximizes the expected cumulative reward. The proposed method is illustrated using a numerical example with repair and inspection maintenance actions. The result shows that as more observations are collected, the learning component progressively learns the true system degradation process, and the planning component adjusts the optimal maintenance policy accordingly as well, which leads to increased reward.
42 ENGINEERING↗
Finite-element-based simulations of electrodes for CO 2 cascade reduction reactions
The multielectron reduction of CO 2 to liquid fuels could be a path to scalable energy storage, but reaching this goal requires major advances in catalysis and systems engineering. Cascade catalysis, which couples sequential reactions without isolating intermediates, has emerged as a promising route to enhance selectivity and efficiency in CO 2 reduction (CO 2 R). In this review, we examine how finite-element-based simulations of continuum model [finite element method (FEM)] approaches are being used to analyze and guide CO 2 R cascade systems. We first outline the fundamentals of cascade catalysis and recent advances in catalytic materials (metallic, molecular, and hybrid architectures). We then focus on FEM developments at the electrode and device scales, emphasizing how these models capture transport phenomena, local microenvironments, and geometry-dependent effects. To clarify design principles, we present case studies of cascade electrodes organized in systems without and with integrated semiconductors. We further emphasize the integration of FEM with multiscale frameworks (density functional theory, molecular dynamics, kinetic Monte Carlo) and its role in bridging atomic-level insights with device-level performance. Finally, we identify current limitations and future prospects, including improved boundary conditions, coupling with operando experiments, and machine learning-accelerated model development. Together, these insights provide design principles for next-generation CO 2 R cascade systems for efficient solar fuel production.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Update to the Performance Assessment for the Savannah River Site Saltstone Disposal Facility - 20124
In 2019, Savannah River Remediation developed a revision to the performance assessment (PA) on behalf of the U.S. Department of Energy (DOE) Savannah River Operations Office (SR) for the near-surface disposal of low-level waste at the Savannah River Site (SRS) Saltstone Disposal Facility (SDF). Soluble waste from SRS Tank Farms undergoes salt processing to remove cesium and other high activity constituents. The low-activity decontaminated salt solution (DSS) is then immobilized by mixing it into a cementitious waste form known as saltstone. After mixing, the saltstone is poured into leak-tight concrete vaults, known as saltstone disposal units (SDUs), where the waste form cures. By the time of facility closure, the SDF is expected to consist of 15 SDUs with a combined capacity of 1.06 E+09 L (280 Mgal) of cured saltstone. The facility operates under a Disposal Authorization Statement (DAS) from DOE and a permit from the South Carolina Department of Health and Environmental Control (SCDHEC). Since the start of operations in 1990, the SDF has received almost 6.7 E+07 L (18 Mgal) of DSS, resulting in the safe disposal of 2.7 E+16 Bq (7.3 E+05 Ci) of activity. Due to the radioactive decay of short-lived contaminants, the total remaining activity in the disposed waste is estimated to be approximately 1.4 E+16 Bq (3.9 E+05 Ci), as of September 2018. The Disposal Authorization Statement requires a demonstration that the system of engineered and natural features of the disposal facility will limit releases from the facility and be protective of human health and the environment for at least the next 1,000 years. The long-term performance of the facility was evaluated under the requirements of the DoE's Radioactive Waste Management Manual (US DOE Manual 435.1-1). Simulations were performed to demonstrate that the disposal facility would meet performance objectives specified in the manual. The evaluation was based on numerical models that simulate the releases of contaminants from the saltstone waste form. Contaminants were transported through groundwater and air pathways to points of assessment to evaluate compliance. In addition, the potential consequences of an inadvertent human intrusion (IHI) were also evaluated. In accordance with the guidance and recommendations in US DoE's technical standard for DAS, deterministic and probabilistic analyses were performed to demonstrate the SDF system, which includes the engineered cover system, the SDUs, the waste form, and the natural features of the site, provides a reasonable expectation that saltstone disposal will meet performance objectives. Post-closure doses to future members of the public (MOP) were evaluated; dose estimates from the air pathway were well below the 1.0 E-04 Sv/yr (10 mrem/yr) performance objective within 1,000 years, and the calculated doses to the MOP from all exposure pathways, including the groundwater and air pathways, were well below the 2.5 E-04 Sv/yr (25 mrem/yr) performance objective within 1,000 years. Doses following an assumed intrusion event within the facility boundaries were well below the IHI performance objectives, where the acute IHI dose was below the acute IHI dose performance objective of 5.0 E-03 Sv (500 mrem) and the chronic IHI dose was below the chronic IHI dose performance objective of 1.0 E-03 Sv/yr (100 mrem/yr) within 1,000 years. (author)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Asymptotic analysis of the fluid flow with a pressure-dependent viscosity in a system of thin pipes
We consider the incompressible fluid with a pressure-dependent viscosity flowing through a multiple pipe system. The viscosity–pressure relation is given by the Barus law commonly used in the engineering applications. Assuming that the ratio between pipes thickness and its length is small, we propose a rigorous asymptotic approach based on the concept of the transformed pressure. As a result, we obtain new macroscopic model describing the effective behavior of the fluid in the system. In particular, the generalized version of the Kirchhoff’s law is derived giving the explicit formula for the junction pressure. The error estimate for the asymptotic approximation is also provided. Mathematical analysis presented here can be applied to a general viscosity–pressure relation satisfied by other empiric laws.
97 MATHEMATICS AND COMPUTING↗
Knowledge-matching based computational framework for genome-scale metabolic model refinement
Genome-scale metabolic models (GEMs) are mathematically structured knowledge base reconstructed from annotated genome of different organisms. With the advancement of next-generation sequencing technology, many organisms have had their genomes sequenced. However, obtaining a high-quality GEM is highly time-consuming, even with the introduction of several genome-scale reconstruction tools that offer automated draft network generation and gap filling. It has been recognized that the iterative process of manual curation and refinement is the limiting step of GEM development, and how to expedite the GEM refinement is still an open question. As cellular metabolism is a complex system with very high degree of freedom and redundancy, the principles and techniques developed in process systems engineering can be adapted to expedite GEM refinement. In this paper we present a knowledge-matching based computation framework for GEM refinement, and demonstrate the effectiveness of the proposed solution using the refinement of a GEM for Clostridium tyrobutyricum.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗